Many monitoring measures were used in the production field for predicting rockburst.However, predicting rock burst according to complicated observation data is alwaysa pressing problem in this research field.Though th...Many monitoring measures were used in the production field for predicting rockburst.However, predicting rock burst according to complicated observation data is alwaysa pressing problem in this research field.Though the critical value method gets extensiveapplication in practice, it stresses only on the superficial change of data and overlooks alot of features of rock burst and useful information that is concealed and hidden in the observationtime series.Pattern recognition extracts the feature value of time domain, frequencydomain and wavelet domain in observation time series to form Multi-Feature vectors,using Euclidean distance measure as the separable criterion between the same typeand different type to compress and transform feature vectors.It applies neural network asa tool to recognize the danger of rock burst, and uses feature vectors being compressedto carry out training and studying.It is proved by test samples that predicting precisionshould be prior to such traditional predicting methods as pattern recognition and critical indicatormethod.展开更多
This paper describes the methodology of singular spectrum analysis (SSA) and demonstratethat it is a powerful method of time series analysis and forecasting,particulary for economic time series.The authors consider th...This paper describes the methodology of singular spectrum analysis (SSA) and demonstratethat it is a powerful method of time series analysis and forecasting,particulary for economic time series.The authors consider the application of SSA to the analysis and forecasting of the Iranian nationalaccounts data as provided by the Central Bank of the Islamic Republic of Iran.展开更多
Accurate and reliable hydrological forecasting is essential for water resource management. Feedforward neural networks can provide satisfactory forecast results in most cases, but traditional gradient-based training a...Accurate and reliable hydrological forecasting is essential for water resource management. Feedforward neural networks can provide satisfactory forecast results in most cases, but traditional gradient-based training algorithms are usually time-consum- ing and may easily converge to local minimum. Hence, how to obtain more appropriate parameters for feedforward neural networks with more precise prediction within shorter time has been a challenging task. Extreme learning machine （ELM）, a new training algorithm for single-hidden layer feedforward neural networks （SLFNs）, has been proposed to avoid these disad- vantages. In this study, a conjunction model of wavelet neural networks with ELM （WNN-ELM） is proposed for 1-month ahead discharge forecasting, The ~ trous wavelet transform is used to decompose the original discharge time series into several sub-series. The sub-series are then used as inputs for SLFNs coupled with ELM algorithm （SLFNs^ELM）; the output is the next step observed discharge. For comparison, the SLFNs-ELM and support vector machine （SVM） are also employed. Monthly discharge time series data from two reservoirs in southwestern China are derive] for validating the models. In addi- tion, four quantitative standard statistical performance evaluation measures are utilized to evaluate the model performance. The results indicate that the SLFNs-ELM performs slightly better than the SVM for peak discharge estimation, and the proposed model WNN-ELM provides more accurate forecast precision than SLFNs-ELM and SVM展开更多
The forecasting of time-series data plays an important role in various domains. It is of significance in theory and application to improve prediction accuracy of the time-series data. With the progress in the study of...The forecasting of time-series data plays an important role in various domains. It is of significance in theory and application to improve prediction accuracy of the time-series data. With the progress in the study of time-series, time-series forecasting model becomes more complicated, and consequently great concern has been drawn to the techniques in designing the forecasting model. A modeling method which is easy to use by engineers and may generate good results is in urgent need. In this paper, a gradient-boost AR ensemble learning algorithm (AREL) is put forward. The effectiveness of AREL is assessed by theoretical analyses, and it is demonstrated that this method can build a strong predictive model by assembling a set of AR models. In order to avoid fitting exactly any single training example, an insensitive loss function is introduced in the AREL algorithm, and accordingly the influence of random noise is reduced. To further enhance the capability of AREL algorithm for non-stationary time-series, improve the robustness of algorithm, discourage overfitting, and reduce sensitivity of algorithm to parameter settings, a weighted kNN prediction method based on AREL algorithm is presented. The results of numerical testing on real data demonstrate that the proposed modeling method and prediction method are effective.展开更多
文摘Many monitoring measures were used in the production field for predicting rockburst.However, predicting rock burst according to complicated observation data is alwaysa pressing problem in this research field.Though the critical value method gets extensiveapplication in practice, it stresses only on the superficial change of data and overlooks alot of features of rock burst and useful information that is concealed and hidden in the observationtime series.Pattern recognition extracts the feature value of time domain, frequencydomain and wavelet domain in observation time series to form Multi-Feature vectors,using Euclidean distance measure as the separable criterion between the same typeand different type to compress and transform feature vectors.It applies neural network asa tool to recognize the danger of rock burst, and uses feature vectors being compressedto carry out training and studying.It is proved by test samples that predicting precisionshould be prior to such traditional predicting methods as pattern recognition and critical indicatormethod.
基金supported by a grant (No. 88/121230) from Institute for Trade StudiesResearch (ITSR), Tehran, Iran
文摘This paper describes the methodology of singular spectrum analysis (SSA) and demonstratethat it is a powerful method of time series analysis and forecasting,particulary for economic time series.The authors consider the application of SSA to the analysis and forecasting of the Iranian nationalaccounts data as provided by the Central Bank of the Islamic Republic of Iran.
基金supported by the National Science Fund for Distinguished Young Scholars,China(Grant No.51025934)the National High-Tech Research and Development Program of China(863 Program)(Grant No.2012AA050205)
文摘Accurate and reliable hydrological forecasting is essential for water resource management. Feedforward neural networks can provide satisfactory forecast results in most cases, but traditional gradient-based training algorithms are usually time-consum- ing and may easily converge to local minimum. Hence, how to obtain more appropriate parameters for feedforward neural networks with more precise prediction within shorter time has been a challenging task. Extreme learning machine （ELM）, a new training algorithm for single-hidden layer feedforward neural networks （SLFNs）, has been proposed to avoid these disad- vantages. In this study, a conjunction model of wavelet neural networks with ELM （WNN-ELM） is proposed for 1-month ahead discharge forecasting, The ~ trous wavelet transform is used to decompose the original discharge time series into several sub-series. The sub-series are then used as inputs for SLFNs coupled with ELM algorithm （SLFNs^ELM）; the output is the next step observed discharge. For comparison, the SLFNs-ELM and support vector machine （SVM） are also employed. Monthly discharge time series data from two reservoirs in southwestern China are derive] for validating the models. In addi- tion, four quantitative standard statistical performance evaluation measures are utilized to evaluate the model performance. The results indicate that the SLFNs-ELM performs slightly better than the SVM for peak discharge estimation, and the proposed model WNN-ELM provides more accurate forecast precision than SLFNs-ELM and SVM
基金supported by the National Natural Science Foundation of China (Grant No. 60974101)Program for New Century Talents of Education Ministry of China (Grant No. NCET-06-0828)
文摘The forecasting of time-series data plays an important role in various domains. It is of significance in theory and application to improve prediction accuracy of the time-series data. With the progress in the study of time-series, time-series forecasting model becomes more complicated, and consequently great concern has been drawn to the techniques in designing the forecasting model. A modeling method which is easy to use by engineers and may generate good results is in urgent need. In this paper, a gradient-boost AR ensemble learning algorithm (AREL) is put forward. The effectiveness of AREL is assessed by theoretical analyses, and it is demonstrated that this method can build a strong predictive model by assembling a set of AR models. In order to avoid fitting exactly any single training example, an insensitive loss function is introduced in the AREL algorithm, and accordingly the influence of random noise is reduced. To further enhance the capability of AREL algorithm for non-stationary time-series, improve the robustness of algorithm, discourage overfitting, and reduce sensitivity of algorithm to parameter settings, a weighted kNN prediction method based on AREL algorithm is presented. The results of numerical testing on real data demonstrate that the proposed modeling method and prediction method are effective.
